Title: Blind noise estimation-based CT image denoising in tetrolet domain

Authors: Manoj Diwakar; Pardeep Kumar

Addresses: Department of Computer Science and Engineering, DIT University, Dehradun, Uttarakhand, India ' Department of Computer Science and Engineering and Information Technology, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

Abstract: Recently in medical imaging, various cases of cancers have been explored because of high dose radiation in computed tomography (CT) scan examinations. These high radiation doses are given to patients to achieve good quality CT images. Instead of increasing radiation dose, an alternate method is required to get high quality images for diagnosis purpose. In this paper, we propose a method where, the noise of CT images will be estimated using patch-based gradient approximation. Further, estimated noise is used to denoise the CT images in tetrolet domain. In proposed scheme, a locally adaptive-based thresholding in tetrolet domain and non-local means filtering have been performed to suppress noise from CT images. Estimation noise from proposed method has been compared from added noise in CT images and it was observed that noise is almost correctly estimated by proposed method. To verify the strength of noise suppression in proposed scheme, comparison with recent other existing methods have been performed. The PSNR and visual quality of experimental results indicate that the proposed scheme gives excellent outcomes in compare to existing schemes.

Keywords: tetrolet transform; non-local means approach; image denoising; computed tomography.

DOI: 10.1504/IJICS.2020.105175

International Journal of Information and Computer Security, 2020 Vol.12 No.2/3, pp.234 - 252

Received: 24 Feb 2018
Accepted: 24 May 2018
Published online: 14 Feb 2020 *

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